Marketing departments quickly adopted big data analytics and obtained good results. Many companies—such as Amazon and its noteworthy and effective personalized marketing powered by big data analytics—use big data–based marketing analytics to outthink their competitors. However, according to a recent survey by Kantar TNS, one of the largest research firms in Europe, delivering meaningful personalized marketing is still a big challenge for organizations.

[Note} This post was published on https://www.linkedin.com/groups/1895501/1895501-6110190381930930179 .

AI is real now, and has created huge impacts on many aspects of our life. Will AI affect research methods and data science greatly? I think so, and every one of us should prepare for it.

This January, I made a presentation at CalTech’s Center for Data Driven Discovery. The subject matter was Research Methods in the Era of Big Data and Automated Discovery. (see http://www.researchmethods.org/AlexLiu_CalTech_Jan21.pdf) For this presentation, I discussed the RM4Es framework, in which four parts of research ensure accurate answers to even the most difficult research challenges: Equation, Estimation, Evaluation, and Explanation. This framework is also a base for AI to come in to assist data science. In future posts, I will discuss this in greater depth.

However, my main argument in this presentation, was that AI and automation are crucial technologies that assist the work of data scientists who are now facing unprecedented pressure from the exponential advance of big data, the increasing complexity of algorithm challenges and the overwhelming demand coming from businesses and governments looking for quick answers to their daily challenges, to name just a few. In fact, without augmentation from AI and some automation, it will quickly become impossible for data scientists to achieve all that is expected of them.

IBM’s Cognitive Automation of Data Science is one of the research automation tools currently here. Another comes from a company called PurePredictive who approached me to see if I’d participate in their beta release of their AI-driven machine learning platform.

Intrigued, I agreed to explore their technology and invited their CEO Justin Reber and Chief Analytics Officer Jason Maughan to speak at one of my meet-ups in California last month at our IBM Glendale office.

After talking with them and exploring their technology, I’m convinced PurePredictive has something special and that their automation is what data scientists need to augment their talents in creating accurate and stable models very quickly. PurePredictive’s patents and development roadmap includes additional game-changing technologies to augment the work of data scientists.

They asked me to help them build a technology advisory board to help shape the future of advanced analytics. I’m excited by both the challenge and opportunity. I believe they are poised to do exactly that.

PurePredictive’s beta is wrapping up today. However, anyone I refer can extend their free access to the platform for two more months. This means that for any models you deploy commercially during that period, PurePredictive will continue to host it for free for as long as those models are actively used by you. That’s a staggering amount of value for you to capture if you take advantage of the offer.

In the coming weeks, I’ll provide more explanation of the 4 Es and my progress in building out a technology advisory board with PurePredictive. As always, I’m interested in your questions, suggestions and overall thoughts about the new data science as augmented by AI !

[Note] Alex Liu is no longer helping PurePredictive now, but continues to develop new research methods, with other tools including the IBM Watson APIs.